Offering flexibility to address challenges through an innovative learning lab design

Have you ever encountered problems in your global health work that you couldn’t solve? Have you felt frustrated because you didn’t have the bandwidth to adapt to challenges while implementing a large-scale project? Or have you had a great idea, but couldn’t find the time to share it? 

If you said “yes” to any of the above, you’re not alone. Our project saw these common obstacles in global health program implementation, and in 2021 we launched a new learning lab to help address them. The Measurement, Adaptive Learning, and Knowledge Management Lab (MAKLab) was developed as a new model to give practitioners the tools, expertise, and flexibility to meet their global health goals. Since launching, we have learned important lessons about how to create a flexible team that supports complex systems and finds shared solutions to challenges. 

What is the problem? Despite careful planning, large-scale global health projects often encounter unanticipated challenges during planning and implementation. Addressing these challenges can require additional expertise, tools, or resources that aren’t built into work plans or budgets. Thus, implementers must sometimes make difficult choices between pulling resources from other priorities, insufficiently addressing these challenges, or making decisions with incomplete information. Additionally, solutions that have already been developed to meet common challenges aren’t always shared broadly with others in the field. New, innovative, and more nimble approaches are needed to ensure that projects have the tools and flexibility to address emerging needs and spread awareness of promising solutions that can improve outcomes. 

What did we do to address this problem? The MOMENTUM Knowledge Accelerator serves as a connector across the USAID-funded MOMENTUM project. MOMENTUM works in partnership with countries to scale up health interventions and improve the overall health and well-being of mothers, children, families, and communities. MOMENTUM Knowledge Accelerator recognized the need for nimbleness in responding to emerging challenges and sharing solutions across MOMENTUM. In response, we developed MAKLab—a first of its kind learning lab helping MOMENTUM projects address persistent, shared, and complex challenges. 

How does it work? MAKLab provides tailored support in response to requests from MOMENTUM project teams when they need additional tools, capacity, or resources to meet needs identified during activity design, start up, or implementation. MOMENTUM teams contact MAKLab directly to share information about their need and request support. MAKLab quickly mobilizes its resources and co-designs an activity addressing the award’s request. When planning activities, MAKLab prioritizes rapid response times and short timelines with the goal of quick and adaptive learning and improvement. MAKLab’s structure allows for flexibility in response to external stresses, unanticipated challenges, or new opportunities. 

The nature, scope, and expertise needed for each activity vary, and thus, each MAKLab activity is unique. The next section provides examples of the types of challenges that MAKLab has responded to and the services that were provided. 

Examples of how MAKLab helped teams work on a challenge

Assessing Provider Capacity

The Challenge: A project that was developing a health facility assessment needed a way to easily assess provider capacity to treat complications during pregnancy, without time-consuming methods like observation.

MAKLab’s Response: MAKLab identified feasible ways to measure capacity, then leveraged clinical expertise to develop vignettes with hypothetical clinical scenarios to allow assessors to evaluate a provider’s capacity to address hypertensive disorders and obstructed labor.

The Results: Vignettes were incorporated into a health facility assessment to be used for ongoing monitoring, evaluation, and learning globally—providing a streamlined assessment methodology for situations where more time-intensive observation methods are not possible.

Adaptive Learning Training

The Challenge: A project was introducing adaptive learning concepts to country teams, but they needed adaptable tools to fit multiple contexts.

MAKLab’s Response: MAKLab identified relevant existing resources and used them to develop a customizable and modularized blended learning training program with pre-recorded content in English and French and a facilitation guide for introducing adaptive learning to country teams.

The Results: The training modules have been used to introduce adaptive learning with projects based in Côte d’Ivoire, Indonesia, Liberia, Madagascar, and Zambia, among others. The training package has played an important role in advancing familiarity with adaptive learning concepts across partner countries.

Blended Learning Training

The Challenge: A project planned to adapt an existing in-person training on voluntary family planning methods to a blended learning format with a digital component, but lacked experience developing digital blended learning training tools and needed guidance on how to begin.

MAKLab’s Response: MAKLab worked with a team of digital health experts to develop guidance and a list of best practices for adapting an existing training to a blended learning format with a digital component. We also compiled an annotated list of examples of related blended learning training content.

The Results: Best practices were used to help inform the development of an existing blended learning training and to develop broader guidance for programs adapting health training content to digital or blended formats.

Increasing Uptake of Toolkits and Guidance

The Challenge: A project was interested in improving evidence-based immunization services by supporting uptake and use of immunization-related toolkits and guidance, but did not know what factors are important for toolkit implementation.

MAKLab’s Response: MAKLab worked with implementation experts to conduct a rapid desk review and qualitative interviews to generate a list of factors that support the uptake and use of immunization-related toolkits and guidance.

The Results: The factors identified in the desk review were used by the project as a framework for evaluating existing immunization toolkits to identify opportunities to improve their design. Findings will help increase uptake of evidence-based resources.

What have we learned? We recognize the importance of dedicated time and resources for systematic learning and ongoing improvement of the MAKLab model. Through our learning exercises to date, we have identified key elements of our model that are important for success, and want to share these lessons with others in related fields:

• Responding quickly to needs: We’ve learned that rapid response times and short timelines were necessary to deliver timely and relevant solutions that could be used by projects. We have therefore designed MAKLab with simple and streamlined processes so that a full activity—from request submission to activity completion—could be completed in three to six months. We have also developed accelerated response mechanisms for urgent requests needing solutions in less than four weeks.  
• Accessing experts for technical input: We’ve learned it’s important to have access to a range of expertise to give credibility to our work and ensure the delivery of high-quality products every time. Therefore, we have established a network of experts who can be flexibly deployed for different activities. Through academic and professional networks, we’ve engaged experts in measurement, implementation science, quality improvement, digital health, neonatology, obstetrics, knowledge management, and human-centered design to inform our work.   
• Prioritizing user needs: We recognize that MAKLab is only successful if it meets needs identified by others within MOMENTUM. A core principle of our work is that MAKLab understands and designs for our users. To this end, we employ a human-centered approach during program design and implementation to remain responsive to user needs. This includes a co-design process during activity design, as well as collecting user feedback after each activity. 
• Setting boundaries: The nature and scope of MAKLab requests are broad, and we cannot solve all challenges that implementers face. We must therefore focus our work where we are likely to have the greatest impact. To achieve this, we have established focus areas of technical expertise and created eligibility criteria to consider and prioritize requests. 
• Flexibly adapting throughout implementation: We have learned that adaptive learning approaches help us learn and improve throughout implementation. At the end of each activity, MAKLab completes a learn phase, using after-action reviews, interviews, and surveys to understand what was successful about processes and products and where improvements could be made. Our learnings are immediately used to modify future activities. This ongoing learning and improvement component has been critical to our learning lab design. 
• Sharing solutions: We have learned that many solutions developed by MAKLab are relevant to others doing related work, and that promoting uptake and use of MAKLab solutions is critical for success. We’ve therefore developed a spread strategy to ensure that planning for dissemination is integrated into all activities and a spread phase at the end of each activity to disseminate relevant products and information.  
• Leveraging the work of others: Many of the solutions that we identify are not our own. Often, we use our additional capacity to find existing knowledge or resources through desk reviews, interviews, or connections with others doing similar work. We regularly ask others for help and seek solutions broadly. This ensures that we aren’t duplicating efforts and are using our resources efficiently. 
• Designing systems and processes to enable success: In order to rapidly respond as needs emerge, appropriately engage experts and users, and continually refine our practices, we need to ensure that our systems and processes are flexible. Therefore, we have built flexibility into our project design. For example, we don’t pre-determine the nature or type of activities we will work on in our work plan, allowing us to respond to requests as they arise. This unique work plan structure required buy-in from our funder, and we deliberately oriented them to the rationale for our approach prior to work plan development. Our funder was also supportive of adaptive learning approaches, which helped secure their backing for this innovative mechanism. 

What does this mean for you? The flexibility to leverage resources and expertise to support adaptive learning as challenges emerge during implementation is critical for program success. This is particularly true in large-scale, complex global health programs. MAKLab offers an example of how flexibility can be integrated into a large global health program by creating additional capacity to support project-identified needs for problem-solving during implementation. Additionally, MAKLab offers a pathway for supporting locally identified problems requiring solutions that can be tailored to the specific context. Learnings from our work may be relevant to others in the global health field, and our lessons may be transferable to similar development programs. By building the capacity to respond rapidly to challenges, access expert networks, flexibly adapt, share solutions, and leverage existing ones, we can help global health programs become more responsive and adaptable–and ultimately more successful.